Measuring and reporting performance of parallel computers constitutes the basis for
scientific advancement of high-performance computing (HPC). Most scientific reports show …

Predictable performance is important for understanding and alleviating application
performance issues; quantifying the effects of source code, compiler, or system software …

Many parallel applications suffer from latent performance limitations that may prevent them
from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only …

I/O is emerging as a major bottleneck for machine learning training, especially in distributed
environments. Indeed, at large scale, I/O takes as much as 85% of training time. Addressing …

In this paper, we present GossipGraD-a gossip communication protocol based Stochastic
Gradient Descent (SGD) algorithm for scaling Deep Learning (DL) algorithms on large-scale …

The allreduce operation is one of the most commonly used communication routines in
distributed applications. To improve its bandwidth and to reduce network traffic, this …

Many tools and techniques have been developed to address specific aspects of interacting
in a virtual world. Few have been designed with an architecture that allows large numbers of …

Optimizing communication performance is imperative for large-scale computing because
communication overheads limit the strong scalability of parallel applications. Today's …

In the generalized minimal residual method (GMRES), the global all-to-all communication
required in each iteration for orthogonalization and normalization of the Krylov base vectors …

The increasing complexity of HPC systems has introduced new sources of variability, which
can contribute to significant differences in run-to-run performance of applications. With …